Anti-money laundering (AML) and combatting terrorist financing (CTF) are positioned as fundamental compliance duties in local and global legislation and regulations. Mitigating AML/CTF risk entails using a risk-based strategy to detect, monitor, and report suspicious actions.
With massive volumes of transactions, constant changes in regulatory requirements, such as scanning against an enormous number of sanctioned entities and PEP lists, and the complicated nature of money laundering activities, AML/CTF technology has become an essential requirement for every compliance function, as the human approach is deemed inefficient.
As a result, to improve their operations, compliance departments require an effective and efficient solution to mitigate AML/CTF risks. The rate of false positives and false negatives are used to determine these systems’ efficiency and efficacy.
Monitoring transactions for suspicious behavior is a critical component of any AML compliance program. Deposits, withdrawals, fund transfers, purchases, merchant credits, and payments are all part of the transaction scope. Monitoring often begins with a rules-based system that monitors client transactions for red flags indicative of money laundering. When a transaction satisfies a predefined rule, an alert is created, and the case is forwarded to the bank’s internal investigation team for human review. If the investigators determine that the activity is consistent with money laundering, the bank will submit a Suspicious Activity Report (SAR) with the related FinCEN.
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Advanced AML Software
AI will considerably enhance accuracy in predicting which situations will result in a SAR file due to its capacity to dynamically learn patterns in complex data. AI anti-money laundering models may be used in the review process to score and rate all new instances. Any case that exceeds a specified risk level is forwarded to investigators for human evaluation. Meanwhile, any case that falls below the threshold can be immediately dismissed or sent to a lower-level review. Once AI models are in production, they may be regularly retrained on new data to detect any unique money laundering activities. The feedback investigators will provide this information.
In general, AI assists investigators in focusing their attention on instances with the highest risk of AML while reducing the amount of time they spend studying false-positive cases. In addition, improvements in the efficacy and efficiency of investigations result in fewer incidents of money laundering that go undiscovered for institutions with high volumes of daily transactions. This enables banks to improve their regulatory compliance while also reducing the importance of financial crime in their network.
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Reducing False Positives
Transaction monitoring and screening methods necessitate the processing and analysis of massive amounts of data from many sources, frequently in an unstructured manner. The more perplexing the data gathered as part of procedural compliance, the more difficult it is to distinguish false positives from actual positive AML alerts.
With the help of Sanction Scanner’s transaction monitoring system, firms may be able to improve their false positive rates by carefully evaluating the data collecting process and properly arranging that data once received. In practice, this involves categorizing names, such as title, first name, and surname, rather than recording each name as a single data item, which is likely to generate administrative uncertainty and impede compliance teams’ attempts to clarify client identities after AML warnings.
Criminals may attempt to alter their names or relocate across countries in order to avoid AML requirements. Keeping this in mind, although the quantity of AML data collected is necessary for creating an accurate client profile, the relevance of that data is critical for the verification process. Firms should ensure that the data they gather about their customers is relevant and timely to the customer’s risk profile, and so assists the AML process. A false positive might be generated, for example, if a company fails to update a customer’s name change or move from a high-risk jurisdiction to a low-risk one.
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March 4, 2022 Published by The Sanction Scanner.
